Apparatus and methods for success probability determination for a user

ABSTRACT

An apparatus for success probability determination for a user is provided. Apparatus may include at least a processor and a memory communicatively connected to the processor. The memory may contain instructions configuring the at least a processor to receive a plurality of criteria; generate indicators as a function of the criteria; receive user specifications, the user specifications comprising credentials of a user; and classify the user specifications to a performance category of a plurality of performance categories based on the user specifications and the indicators.

FIELD OF THE INVENTION

The present invention generally relates to the field of postings. Inparticular, the present invention is directed to apparatus and methodsfor success probability determination for a user.

BACKGROUND

Predicting a success of users for a given posting is difficult toachieve with precision.

SUMMARY OF THE DISCLOSURE

In an aspect of the present disclosure is an apparatus for successprobability determination for a user, the apparatus including at least aprocessor; a memory communicatively connected to the processor, thememory containing instructions configuring the at least a processor toreceive a plurality of criteria; generate indicators as a function ofthe criteria; receive user specifications, the user specificationscomprising credentials of a user; and classify the user specificationsto a performance category of a plurality of performance categories basedon the user specifications and the indicators.

In another aspect of the present disclosure is a method for successprobability determination for a user including receiving, at aprocessor, a plurality of criteria; generating, by the processor,indicators as a function of the criteria; receiving, at the processor,user specifications, the user specifications comprising credentials of auser; classifying, by the processor using a classifier, the userspecifications to a performance category of a plurality of performancecategories based on the user specifications and the indicators.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an embodiment of an apparatus forpredicting success for a user;

FIG. 2 illustrates an exemplary neural network;

FIG. 3 is a block diagram of an exemplary node;

FIG. 4 is a block diagram of an exemplary immutable sequential listing;

FIG. 5 is a flow diagram of an exemplary method for predicting successfor a user;

FIG. 6 is a block diagram of exemplary machine-learning processes; and

FIG. 7 is a block of exemplary fuzzy set comparison;

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed toapparatus and methods for success probability determination for a user.In an embodiment, an apparatus includes at least a processor and amemory communicatively connected to the processor. The memory maycontain instructions configuring the at least a processor to receive aplurality of criteria; generate indicators as a function of thecriteria; receive user specifications, the user specificationscomprising credentials of a user; and classify the user specificationsto a performance category of a plurality of performance categories basedon the user specifications and the indicators. The aptitude score maymeasure the likelihood of success for a user with a posting. Theprocessor may implement machine-learning modules to generate theindicators. Exemplary embodiments illustrating aspects of the presentdisclosure are described below in the context of several specificexamples.

In an embodiment, methods and systems described herein may perform orimplement one or more aspects of a cryptographic system. In oneembodiment, a cryptographic system is a system that converts data from afirst form, known as “plaintext,” which is intelligible when viewed inits intended format, into a second form, known as “ciphertext,” which isnot intelligible when viewed in the same way. Ciphertext may beunintelligible in any format unless first converted back to plaintext.In one embodiment, a process of converting plaintext into ciphertext isknown as “encryption.” Encryption process may involve the use of adatum, known as an “encryption key,” to alter plaintext. Cryptographicsystem may also convert ciphertext back into plaintext, which is aprocess known as “decryption.” Decryption process may involve the use ofa datum, known as a “decryption key,” to return the ciphertext to itsoriginal plaintext form. In embodiments of cryptographic systems thatare “symmetric,” decryption key is essentially the same as encryptionkey: possession of either key makes it possible to deduce the other keyquickly without further secret knowledge. Encryption and decryption keysin symmetric cryptographic systems may be kept secret and shared onlywith persons or entities that the user of the cryptographic systemwishes to be able to decrypt the ciphertext. One example of a symmetriccryptographic system is the Advanced Encryption Standard (“AES”), whicharranges plaintext into matrices and then modifies the matrices throughrepeated permutations and arithmetic operations with an encryption key.

In embodiments of cryptographic systems that are “asymmetric,” eitherencryption or decryption key cannot be readily deduced withoutadditional secret knowledge, even given the possession of acorresponding decryption or encryption key, respectively; a commonexample is a “public key cryptographic system,” in which possession ofthe encryption key does not make it practically feasible to deduce thedecryption key, so that the encryption key may safely be made availableto the public. An example of a public key cryptographic system is RSA,in which an encryption key involves the use of numbers that are productsof very large prime numbers, but a decryption key involves the use ofthose very large prime numbers, such that deducing the decryption keyfrom the encryption key requires the practically infeasible task ofcomputing the prime factors of a number which is the product of two verylarge prime numbers. Another example is elliptic curve cryptography,which relies on the fact that given two points P and Q on an ellipticcurve over a finite field, and a definition for addition where A+B=−R,the point where a line connecting point A and point B intersects theelliptic curve, where “0,” the identity, is a point at infinity in aprojective plane containing the elliptic curve, finding a number k suchthat adding P to itself k times results in Q is computationallyimpractical, given correctly selected elliptic curve, finite field, andP and Q.

In some embodiments, systems and methods described herein producecryptographic hashes, also referred to by the equivalent shorthand term“hashes.” A cryptographic hash, as used herein, is a mathematicalrepresentation of a lot of data, such as files or blocks in a blockchain as described in further detail below; the mathematicalrepresentation is produced by a lossy “one-way” algorithm known as a“hashing algorithm.” Hashing algorithm may be a repeatable process; thatis, identical lots of data may produce identical hashes each time theyare subjected to a particular hashing algorithm. Because hashingalgorithm is a one-way function, it may be impossible to reconstruct alot of data from a hash produced from the lot of data using the hashingalgorithm. In the case of some hashing algorithms, reconstructing thefull lot of data from the corresponding hash using a partial set of datafrom the full lot of data may be possible only by repeatedly guessing atthe remaining data and repeating the hashing algorithm; it is thuscomputationally difficult if not infeasible for a single computer toproduce the lot of data, as the statistical likelihood of correctlyguessing the missing data may be extremely low. However, the statisticallikelihood of a computer of a set of computers simultaneously attemptingto guess the missing data within a useful timeframe may be higher,permitting mining protocols as described in further detail below.

In an embodiment, hashing algorithm may demonstrate an “avalancheeffect,” whereby even extremely small changes to lot of data producedrastically different hashes. This may thwart attempts to avoid thecomputational work necessary to recreate a hash by simply inserting afraudulent datum in data lot, enabling the use of hashing algorithms for“tamper-proofing” data such as data contained in an immutable ledger asdescribed in further detail below. This avalanche or “cascade” effectmay be evinced by various hashing processes; persons skilled in the art,upon reading the entirety of this disclosure, will be aware of varioussuitable hashing algorithms for purposes described herein. Verificationof a hash corresponding to a lot of data may be performed by running thelot of data through a hashing algorithm used to produce the hash. Suchverification may be computationally expensive, albeit feasible,potentially adding up to significant processing delays where repeatedhashing, or hashing of large quantities of data, is required, forinstance as described in further detail below. Examples of hashingprograms include, without limitation, SHA256, a NIST standard; furthercurrent and past hashing algorithms include Winternitz hashingalgorithms, various generations of Secure Hash Algorithm (including“SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as“MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny(e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), MessageAuthentication Code (“MAC”)—family hash functions such as PMAC, OMAC,VMAC, HMAC, and UMAC, Polyl305-AES, Elliptic Curve Only Hash (“ECOH”)and similar hash functions, Fast-Syndrome-based (FSB) hash functions,GOST hash functions, the Grøstl hash function, the HAS-160 hashfunction, the JH hash function, the RadioGatún hash function, the Skeinhash function, the Streebog hash function, the SWIFFT hash function, theTiger hash function, the Whirlpool hash function, or any hash functionthat satisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of O(2^(n/2)) for n outputbits; thus, it may take on the order of 2²⁵⁶ operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

A “secure proof,” as used in this disclosure, is a protocol whereby anoutput is generated that demonstrates possession of a secret, such asdevice-specific secret, without demonstrating the entirety of thedevice-specific secret; in other words, a secure proof by itself, isinsufficient to reconstruct the entire device-specific secret, enablingthe production of at least another secure proof using at least adevice-specific secret. A secure proof may be referred to as a “proof ofpossession” or “proof of knowledge” of a secret. Where at least adevice-specific secret is a plurality of secrets, such as a plurality ofchallenge-response pairs, a secure proof may include an output thatreveals the entirety of one of the plurality of secrets, but not all ofthe plurality of secrets; for instance, secure proof may be a responsecontained in one challenge-response pair. In an embodiment, proof maynot be secure; in other words, proof may include a one-time revelationof at least a device-specific secret, for instance as used in a singlechallenge-response exchange.

Secure proof may include a zero-knowledge proof, which may provide anoutput demonstrating possession of a secret while revealing none of thesecret to a recipient of the output; zero-knowledge proof may beinformation-theoretically secure, meaning that an entity with infinitecomputing power would be unable to determine secret from output.Alternatively, zero-knowledge proof may be computationally secure,meaning that determination of secret from output is computationallyinfeasible, for instance to the same extent that determination of aprivate key from a public key in a public key cryptographic system iscomputationally infeasible. Zero-knowledge proof algorithms maygenerally include a set of two algorithms, a prover algorithm, or “P,”which is used to prove computational integrity and/or possession of asecret, and a verifier algorithm, or “V” whereby a party may check thevalidity of P. Zero-knowledge proof may include an interactivezero-knowledge proof, wherein a party verifying the proof must directlyinteract with the proving party; for instance, the verifying and provingparties may be required to be online, or connected to the same networkas each other, at the same time. Interactive zero-knowledge proof mayinclude a “proof of knowledge” proof, such as a Schnorr algorithm forproof on knowledge of a discrete logarithm in a Schnorr algorithm, aprover commits to a randomness r, generates a message based on r, andgenerates a message adding r to a challenge c multiplied by a discretelogarithm that the prover is able to calculate; verification isperformed by the verifier who produced c by exponentiation, thuschecking the validity of the discrete logarithm. Interactivezero-knowledge proofs may alternatively or additionally include sigmaprotocols. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various alternative interactivezero-knowledge proofs that may be implemented consistently with thisdisclosure.

Alternatively, zero-knowledge proof may include a non-interactivezero-knowledge, proof, or a proof wherein neither party to the proofinteracts with the other party to the proof; for instance, each of aparty receiving the proof and a party providing the proof may receive areference datum which the party providing the proof may modify orotherwise use to perform the proof. As a non-limiting example,zero-knowledge proof may include a succinct non-interactive arguments ofknowledge (ZK-SNARKS) proof, wherein a “trusted setup” process createsproof and verification keys using secret (and subsequently discarded)information encoded using a public key cryptographic system, a proverruns a proving algorithm using the proving key and secret informationavailable to the prover, and a verifier checks the proof using theverification key; public key cryptographic system may include RSA,elliptic curve cryptography, ElGamal, or any other suitable public keycryptographic system. Generation of trusted setup may be performed usinga secure multiparty computation so that no one party has control of thetotality of the secret information used in the trusted setup; as aresult, if any one party generating the trusted setup is trustworthy,the secret information may be unrecoverable by malicious parties. Asanother non-limiting example, non-interactive zero-knowledge proof mayinclude a Succinct Transparent Arguments of Knowledge (ZK-STARKS)zero-knowledge proof. In an embodiment, a ZK-STARKS proof includes aMerkle root of a Merkle tree representing evaluation of a secretcomputation at some number of points, which may be 1 billion points,plus Merkle branches representing evaluations at a set of randomlyselected points of the number of points; verification may includedetermining that Merkle branches provided match the Merkle root, andthat point verifications at those branches represent valid values, wherevalidity is shown by demonstrating that all values belong to the samepolynomial created by transforming the secret computation. In anembodiment, ZK-STARKS does not require a trusted setup.

Zero-knowledge proof may include any other suitable zero-knowledgeproof. Zero-knowledge proof may include, without limitationbulletproofs. Zero-knowledge proof may include a homomorphic public-keycryptography (hPKC)-based proof. Zero-knowledge proof may include adiscrete logarithmic problem (DLP) proof. Zero-knowledge proof mayinclude a secure multi-party computation (MPC) proof. Zero-knowledgeproof may include, without limitation, an incrementally verifiablecomputation (IVC). Zero-knowledge proof may include an interactiveoracle proof (IOP). Zero-knowledge proof may include a proof based onthe probabilistically checkable proof (PCP) theorem, including a linearPCP (LPCP) proof. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms ofzero-knowledge proofs that may be used, singly or in combination,consistently with this disclosure.

In an embodiment, secure proof is implemented using a challenge-responseprotocol. In an embodiment, this may function as a one-time padimplementation; for instance, a manufacturer or other trusted party mayrecord a series of outputs (“responses”) produced by a device possessingsecret information, given a series of corresponding inputs(“challenges”), and store them securely. In an embodiment, achallenge-response protocol may be combined with key generation. Asingle key may be used in one or more digital signatures as described infurther detail below, such as signatures used to receive and/or transferpossession of crypto-currency assets; the key may be discarded forfuture use after a set period of time. In an embodiment, varied inputsinclude variations in local physical parameters, such as fluctuations inlocal electromagnetic fields, radiation, temperature, and the like, suchthat an almost limitless variety of private keys may be so generated.Secure proof may include encryption of a challenge to produce theresponse, indicating possession of a secret key. Encryption may beperformed using a private key of a public key cryptographic system, orusing a private key of a symmetric cryptographic system; for instance,trusted party may verify response by decrypting an encryption ofchallenge or of another datum using either a symmetric or public-keycryptographic system, verifying that a stored key matches the key usedfor encryption as a function of at least a device-specific secret. Keysmay be generated by random variation in selection of prime numbers, forinstance for the purposes of a cryptographic system such as RSA thatrelies prime factoring difficulty. Keys may be generated by randomizedselection of parameters for a seed in a cryptographic system, such aselliptic curve cryptography, which is generated from a seed. Keys may beused to generate exponents for a cryptographic system such asDiffie-Helman or ElGamal that are based on the discrete logarithmproblem.

A “digital signature,” as used herein, includes a secure proof ofpossession of a secret by a signing device, as performed on providedelement of data, known as a “message.” A message may include anencrypted mathematical representation of a file or other set of datausing the private key of a public key cryptographic system. Secure proofmay include any form of secure proof as described above, includingwithout limitation encryption using a private key of a public keycryptographic system as described above. Signature may be verified usinga verification datum suitable for verification of a secure proof; forinstance, where secure proof is enacted by encrypting message using aprivate key of a public key cryptographic system, verification mayinclude decrypting the encrypted message using the corresponding publickey and comparing the decrypted representation to a purported match thatwas not encrypted; if the signature protocol is well-designed andimplemented correctly, this means the ability to create the digitalsignature is equivalent to possession of the private decryption keyand/or device-specific secret. Likewise, if a message making up amathematical representation of file is well-designed and implementedcorrectly, any alteration of the file may result in a mismatch with thedigital signature; the mathematical representation may be produced usingan alteration-sensitive, reliably reproducible algorithm, such as ahashing algorithm as described above. A mathematical representation towhich the signature may be compared may be included with signature, forverification purposes; in other embodiments, the algorithm used toproduce the mathematical representation may be publicly available,permitting the easy reproduction of the mathematical representationcorresponding to any file.

In some embodiments, digital signatures may be combined with orincorporated in digital certificates. In one embodiment, a digitalcertificate is a file that conveys information and links the conveyedinformation to a “certificate authority” that is the issuer of a publickey in a public key cryptographic system. Certificate authority in someembodiments contains data conveying the certificate authority'sauthorization for the recipient to perform a task. The authorization maybe the authorization to access a given datum. The authorization may bethe authorization to access a given process. In some embodiments, thecertificate may identify the certificate authority. The digitalcertificate may include a digital signature.

In some embodiments, a third party such as a certificate authority (CA)is available to verify that the possessor of the private key is aparticular entity; thus, if the certificate authority may be trusted,and the private key has not been stolen, the ability of an entity toproduce a digital signature confirms the identity of the entity andlinks the file to the entity in a verifiable way. Digital signature maybe incorporated in a digital certificate, which is a documentauthenticating the entity possessing the private key by authority of theissuing certificate authority and signed with a digital signaturecreated with that private key and a mathematical representation of theremainder of the certificate. In other embodiments, digital signature isverified by comparing the digital signature to one known to have beencreated by the entity that purportedly signed the digital signature; forinstance, if the public key that decrypts the known signature alsodecrypts the digital signature, the digital signature may be consideredverified. Digital signature may also be used to verify that the file hasnot been altered since the formation of the digital signature.

Now referring to FIG. 1 , an apparatus for success probabilitydetermination for a user is illustrated. Apparatus 100 includes aprocessor 104. Processor 104 may include any computing device asdescribed in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Processor 104 may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Processor104 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting processor 104 toone or more of a variety of networks, and one or more devices. Examplesof a network interface device include, but are not limited to, a networkinterface card (e.g., a mobile network interface card, a LAN card), amodem, and any combination thereof. Examples of a network include, butare not limited to, a wide area network (e.g., the Internet, anenterprise network), a local area network (e.g., a network associatedwith an office, a building, a campus or other relatively smallgeographic space), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.Processor 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Processor 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Processor 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Processor 104 may be implemented usinga “shared nothing” architecture in which data is cached at the worker,in an embodiment, this may enable scalability of apparatus 100 and/orcomputing device.

With continued reference to FIG. 1 , processor 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, processor 104 maybe configured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Processor 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing. Apparatus 100 may include a memory 108.Memory 108 may be communicatively connected to processor 104 and may beconfigured to store information and/or datum related to apparatus 100.In one or more embodiments, memory 108 may be communicatively connectedto processor 104 and configured to contain instructions configuring theprocessor 104 to execute any operations discussed in this disclosure. Inone or more embodiments, memory 108 may include a storage device, asdescribed further in this disclosure below.

As used in this disclosure, “communicatively connected” means connectedby way of a connection, attachment or linkage between two or more relatewhich allows for reception and/or transmittance of informationtherebetween. For example, and without limitation, this connection maybe wired or wireless, direct or indirect, and between two or morecomponents, circuits, devices, systems, and the like, which allows forreception and/or transmittance of data and/or signal(s) therebetween.Data and/or signals therebetween may include, without limitation,electrical, electromagnetic, magnetic, video, audio, radio and microwavedata and/or signals, combinations thereof, and the like, among others. Acommunicative connection may be achieved, for example and withoutlimitation, through wired or wireless electronic, digital or analog,communication, either directly or by way of one or more interveningdevices or components. Further, communicative connection may includeelectrically coupling or connecting at least an output of one device,component, or circuit to at least an input of another device, component,or circuit. For example, and without limitation, via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may also include indirect connections via, forexample and without limitation, wireless connection, radiocommunication, low power wide area network, optical communication,magnetic, capacitive, or optical coupling, and the like. In someinstances, the terminology “communicatively coupled” may be used inplace of communicatively connected in this disclosure.

Still referring to FIG. 1 , processor 104 may be configured to receive aposting 112 including criteria 116. In some embodiments, processor 104may receive a plurality of criteria 116. Processor 104 may becommunicatively connected to a network, as discussed above.

“Communicatively connected,” for the purposes of this disclosure, is aprocess whereby one device, component, or circuit is able to receivedata from and/or transmit data to another device, component, or circuit.Communicative connection may be performed by wired or wirelesselectronic communication, either directly or by way of one or moreintervening devices or components. In an embodiment, communicativeconnection includes electrically connection an output of one device,component, or circuit to an input of another device, component, orcircuit. Communicative connection may be performed via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connection may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, or optical connection, or the like.In an embodiment, communicative connecting may include electricallyconnecting an output of one device, component, or circuit to an input ofanother device, component, or circuit. Communicative connecting may beperformed via a bus or other facility for intercommunication betweenelements of a computing device. Communicative connecting may includeindirect connections via “wireless” connection, low power wide areanetwork, radio communication, optical communication, magnetic,capacitive, or optical connection, or the like.

With continued reference to FIG. 1 , network may include one or moreother processors. A “posting,” as used in this disclosure, is acommunication of a job position for which an employer is seeking or maybe seeking one or more candidates to potentially fill the job position.As used in this disclosure, a user may be an employee or prospectiveemployee who may view and/or apply to postings 112. Processor 104 mayreceive a plurality of postings 112. A posting may include informationabout the employer such as the employer's name and address; compensationsuch as a salary, an hourly wage, and/or benefits; a title of the jobposition; geographical location of where the job will be performedand/or whether the job is to be performed remotely; a description of thejob position such as a listing of responsibilities, expectations, and/orgoals to be accomplished; criteria; and/or the like. A job position maybe part-time and/or full-time. Job position may be as an employee and/orcontractor. As used in this disclosure, “criteria,” are skills,accreditations, a minimum grade point average (GPA), degree, majorand/or focus of study, and/or experience. Criteria 116 may includerequirements and/or preferences. As used in this disclosure, a“requirement” is a criterion that is critical for a prospective employeeto be eligible for consideration for a posting. In some embodiments,requirements must be satisfied to be eligible for a posting. As used inthis disclosure, a “preference” is a desired criterion, but it is notrequired for a prospective employee to be considered for a posting.

Still referring to FIG. 1 , processor 104 may be configured to generateindicators 120 as a function of criteria 116, such as the criteria inposting 112. As used in this disclosure, “indicators” are factors thatmeasure a likelihood a user may be hired for a posting and successful inthe position. Indicators 120 may include skills such as managerialskills and networking skills, accreditations, a grade point average(GPA), degree, major and/or focus of study, prior employment,experience, and/or the like. Indicators 120 may be based on the posting112, such as an industry the posting 112 is in, field the posting 112 isin, level of position, title of position, expected salary, skillrequirements, responsibilities, location of position, criteria 116,and/or the like. For example, indicators 120 for posting 112 for a salesposition may include the amount of experience in sales, communicationskills, achievements or awards earned, complexity of goods or servicessold, and/or the like. As another example, indicators 120 for posting112 for associate position in architecture may include the amount ofexperience as an architect, accreditation by National ArchitecturalAccrediting Board, type of experience such as commercial or residential,types of projects and/or buildings, size of architecture firms userpreviously worked for, and/or the like.

Still referring to FIG. 1 , processor 104 may generate indicators 120 byutilizing one or more algorithms or generating one or moremachine-learning modules, such as indicator module 124. Indicator module124 may also determine a weight of indicators 120. Indicator module 124may include utilizing a classifier and/or a machine-learning model asdiscussed in reference to FIG. 6 . In one or more embodiments, amachine-learning module may be generated using training data. Trainingdata may include inputs and corresponding predetermined outputs so thatmachine-learning module may use the correlations between the providedexemplary inputs and outputs to develop an algorithm and/or relationshipthat then allows machine-learning module to determine its own outputsfor inputs. Training data may contain correlations that amachine-learning process may use to model relationships between two ormore categories of data elements. The exemplary inputs and outputs maycome from a database, such as any database described in this disclosure,or be provided by a user such as a prospective employee, and/or anemployer. In other embodiments, machine-learning module may obtain atraining set by querying a communicatively connected database thatincludes past inputs and outputs. Training data may include inputs fromvarious types of databases, resources, and/or user inputs and outputscorrelated to each of those inputs so that a machine-learning module maydetermine an output. Correlations may indicate causative and/orpredictive links between data, which may be modeled as relationships,such as mathematical relationships, by machine-learning processes, asdescribed in further detail below. In one or more embodiments, trainingdata may be formatted and/or organized by categories of data elementsby, for example, associating data elements with one or more descriptorscorresponding to categories of data elements. As a non-limiting example,training data may include data entered in standardized forms by personsor processes, such that entry of a given data element in a given fieldin a form may be mapped to one or more descriptors of categories.Elements in training data may be linked to descriptors of categories bytags, tokens, or other data elements. Indicator module 124 may begenerated using training data, such as indicator data. Indicator module124 may be trained by the correlated inputs and outputs of indicatordata. Inputs of indicator data may include postings 112. Outputs oftraining data may include indicators 120 corresponding to the inputs.Indicator data may be postings 112 and corresponding indicators 120 thathave already been determined whether manually, by machine, or any othermethod. Indicator data may be gathered from employer feedback, such asclarification of what criteria 116 for postings 112 are, which of thecriteria 116 are requirements, which of the criteria 116 arepreferences, and/or a ranking or weighting of each of the criteria 116.Indicator data may also include feedback from employers and/or user whowere placed in positions to determine the success of the placement basedand commonalities of indicators 120 among users who were successfullyplaced in positions of postings 112. Indicator data may include previousoutputs such that indicator module 124 iteratively produces outputs,thus creating a feedback loop. Indicator module 124 using amachine-learning process may output indicators 120 based on input ofposting 112 and indicator data. In some embodiments, indicators 120 maybe weighted and/or ranked according to the importance of the indicators120. For example, an applicant's school grades may be less of apredicting factor than amount of sales experience for whether theapplicant will likely get a sales position the user applies to and/orwhether the user will be successful in the sales position. Thus, inposting 112 for a sales position, indicators 120 of user's GPA may beweighed less and/or ranked lower than indicators 120 of user's years ofexperience in sales. Indicators 120 may be weighted based on therequirements and/or preferences of criteria 116. For example, indicators120 that reflect the requirements may be weighted more than indicators120 that reflect the preferences. Indicator module 124 may be configuredto output indicators 120 that are weighted and/or ranked based onweighted and/or ranked indicators 120 as outputs in indicator data. Insome embodiments, indicators 120 are weighted and/or ranked manually,such as by employers. In some embodiments, indicators 120 may beweighted and/or ranked based on training data, such as indicator data,and the strength specific indicators 120 present among users correspondto successful applicants of posting 112 and/or successful workers hiredfor the posting 112.

With continued reference to FIG. 1 , processor 104 may be configured toreceive user specifications 128 including credentials 132 of user. Asused in this disclosure, “user specifications” are one or morecommunications that includes information about the user. Userspecifications may be documents, audio files, and/or digital filesincluding video files. User specifications may include a transcript froman educational institution the user attended such as a collegetranscript, a resume, a letter of recommendation, certifications, and/orthe like. As used in this disclosure, “credentials” are informationabout a prospective employee pertaining to qualifications of theprospective employee and may include, for example, skills,accreditations, a minimum grade point average (GPA), degree, majorand/or focus of study, prior employment, experience, and/or the like. Insome cases, credentials 132 may be explicitly conveyed within userspecifications 128. Alternatively, or additionally, in some cases,credentials 132 may be conveyed implicitly in user specifications 128.Processor 104 may be configured to store user specifications 128 in adatabase and/or memory 108 and retrieve the user specifications 128.Processor 104 may be communicatively connected to a user device andreceive user specifications 128 from the user device. As used in thisdisclosure, a “user device” is a computing device controlled and/oroperated by a user. Computing device may be any computing devicedescribed in this disclosure, such as a processor communicativelyconnected to a memory. User device may be a personal computer such as adesktop, laptop, smart phone, and/or the like. Processor 104 may beconfigured to require information from user device, such as a usernameand a password, to verify the identity of user. Processor 104 may sendverification to user, such as an email to user's email address and/or atext message to user's phone saved on a memory and/or database to whichprocessor 104 has access. Verification may include a link to click thatsends a verification to processor 104. Verification may include atemporary code for user to then input from user device to confirm thatthe device is user device.

Still referring to FIG. 1 , user specifications 128 may include a videorecord 136. As used in this disclosure, a “video record” is dataincluding an audio recording of a prospective employee for purposes ofpotentially acquiring a job. The audio recording may include verbalcontent 140. As used in this disclosure, “verbal content” iscomprehensible language-based communication. For example, verbal content140 may include a monologue. Video record 136 may also include a visualrecording of the prospective employee. Visual recording may include animage component 144. As used in this disclosure, “image component” maybe a visual representation of information, such as a plurality oftemporally sequential frames and/or pictures, related to video record136. For example, image component 144 may include animations, stillimagery, recorded video, and the like. Video record 136 may becommunicated by way of digital signals, for example between computingdevices which are communicatively connected with at least a wirelessnetwork. Video record 136 may be compressed to optimize speed and/orcost of transmission of video. Video record 136 may be compressedaccording to a video compression coding format (i.e., codec). Exemplaryvideo compression codecs include H.26x codecs, MPEG formats, VVC,SVT-AV1, and the like. In some cases, compression of a digital video maybe lossy, in which some information may be lost during compression.Alternatively, or additionally, in some cases, compression of a videorecord 136 may be substantially lossless, where substantially noinformation is lost during compression. Processor 104 may receiveposting 112 and/or video record 136 from an employer, hiring agency,recruiting firm, and/or a prospective employee. Processor 104 mayreceive posting 112 and/or video record 136 from a computing devicethrough a network, from a database, and or store posting 112 and/orvideo record 136 in a memory and retrieve from the memory. Apparatus 100may include a memory 108. Memory 108 may be configured to storeinformation and/or datum related to apparatus 100, such as posting 112including criteria 116 and/or user specifications 128 includingcredentials 132.

Still referring to FIG. 1 , processor 104 may be configured to extract aplurality of textual elements 140 from user specifications 128, such asvideo record 136, which may include credentials 132. Processor 104 mayinclude audiovisual speech recognition (AVSR) processes to recognizeverbal content 140 in user specifications 128. For example, processor104 may use image component 144 to aid in recognition of audible verbalcontent 140 such as viewing prospective employee move their lips tospeak on video to process the audio content of user specifications 128.AVSR may use image component 144 to aid the overall translation of theaudio verbal content 140 of user specifications 128. In someembodiments, AVSR may include techniques employing image processingcapabilities in lip reading to aid speech recognition processes. In somecases, AVSR may be used to decode (i.e., recognize) indeterministicphonemes or help in forming a preponderance among probabilisticcandidates. In some cases, AVSR may include an audio-based automaticspeech recognition process and an image-based automatic speechrecognition process. AVSR may combine results from both processes withfeature fusion. Audio-based speech recognition process may analysisaudio according to any method described herein, for instance using a Melfrequency cepstral coefficients (MFCCs) and/or log-Mel spectrogramderived from raw audio samples. Image-based speech recognition mayperform feature recognition to yield an image vector. In some cases,feature recognition may include any feature recognition processdescribed in this disclosure, for example a variant of a convolutionalneural network. In some cases, AVSR employs both an audio datum and animage datum to recognize verbal content 140. For instance, audio vectorand image vector may each be concatenated and used to predict speechmade by prospective employee, who is ‘on camera.’

Still referring to FIG. 1 , processor 104 may be configured to identifya plurality of credentials 132 from user specifications 128. In somecases, processor 104 may be configured to recognize at least a keywordas a function of visual verbal content 140. In some cases, recognizingat least keyword may include optical character recognition. As used inthis disclosure, a “keyword” is an element of word or syntax used toidentify and/or match elements to each other. At least a keyword mayinclude credentials 132 and/or indicators 120. In some cases, processor104 may generate a transcript of much or even all verbal content 140from user specifications 128. Processor 104 may use transcript toanalyze the content of user specifications 128 and extract credentials132.

Still referring to FIG. 1 , in some embodiments, optical characterrecognition or optical character reader (OCR) may include automaticconversion of images of written (e.g., typed, handwritten or printedtext) into machine-encoded text. In some cases, recognition of at leasta keyword from an image component 144 may include one or more processes,including without limitation optical character recognition (OCR),optical word recognition, intelligent character recognition, intelligentword recognition, and the like. In some cases, OCR may recognize writtentext, one glyph or character at a time. In some cases, optical wordrecognition may recognize written text, one word at a time, for example,for languages that use a space as a word divider. In some cases,intelligent character recognition (ICR) may recognize written text oneglyph or character at a time, for instance by employing machine-learningprocesses. In some cases, intelligent word recognition (IWR) mayrecognize written text, one word at a time, for instance by employingmachine-learning processes.

Still referring to FIG. 1 , in some cases OCR may be an “offline”process, which analyses a static document or image frame. In some cases,handwriting movement analysis can be used as input to handwritingrecognition. For example, instead of merely using shapes of glyphs andwords, this technique may capture motions, such as the order in whichsegments are drawn, the direction, and the pattern of putting the pendown and lifting it. This additional information may make handwritingrecognition more accurate. In some cases, this technology may bereferred to as “online” character recognition, dynamic characterrecognition, real-time character recognition, and intelligent characterrecognition.

Still referring to FIG. 1 , in some cases, OCR processes may employpre-processing of image component 144. Pre-processing process mayinclude without limitation de-skew, de-speckle, binarization, lineremoval, layout analysis or “zoning,” line and word detection, scriptrecognition, character isolation or “segmentation,” and normalization.In some cases, a de-skew process may include applying a transform (e.g.,homography or affine transform) to image component 144 to align text. Insome cases, a de-speckle process may include removing positive andnegative spots and/or smoothing edges. In some cases, a binarizationprocess may include converting an image from color or greyscale toblack-and-white (i.e., a binary image). Binarization may be performed asa simple way of separating text (or any other desired image component)from a background of image component 144. In some cases, binarizationmay be required for example if an employed OCR algorithm only works onbinary images. In some cases, a line removal process may include removalof non-glyph or non-character imagery (e.g., boxes and lines). In somecases, a layout analysis or “zoning” process may identify columns,paragraphs, captions, and the like as distinct blocks. In some cases, aline and word detection process may establish a baseline for word andcharacter shapes and separate words, if necessary. In some cases, ascript recognition process may, for example in multilingual documents,identify script allowing an appropriate OCR algorithm to be selected. Insome cases, a character isolation or “segmentation” process may separatesignal characters, for example character-based OCR algorithms. In somecases, a normalization process may normalize aspect ratio and/or scaleof image component 144.

Still referring to FIG. 1 , in some embodiments an OCR process mayinclude an OCR algorithm. Exemplary OCR algorithms include matrixmatching process and/or feature extraction processes. Matrix matchingmay involve comparing an image to a stored glyph on a pixel-by-pixelbasis. In some case, matrix matching may also be known as “patternmatching,” “pattern recognition,” and/or “image correlation.” Matrixmatching may rely on an input glyph being correctly isolated from therest of the image component 144. Matrix matching may also rely on astored glyph being in a similar font and at a same scale as input glyph.Matrix matching may work best with typewritten text.

Still referring to FIG. 1 , in some embodiments, an OCR process mayinclude a feature extraction process. In some cases, feature extractionmay decompose a glyph into at least a feature. Exemplary non-limitingfeatures may include corners, edges, lines, closed loops, linedirection, line intersections, and the like. In some cases, featureextraction may reduce dimensionality of representation and may make therecognition process computationally more efficient. In some cases,extracted feature may be compared with an abstract vector-likerepresentation of a character, which might reduce to one or more glyphprototypes. General techniques of feature detection in computer visionare applicable to this type of OCR. In some embodiments,machine-learning processes like nearest neighbor classifiers (e.g.,k-nearest neighbors algorithm) may be used to compare image featureswith stored glyph features and choose a nearest match. OCR may employany machine-learning process described in this disclosure, for examplemachine-learning processes described with reference to FIG. 2 .Exemplary non-limiting OCR software includes Cuneiform and Tesseract.Cuneiform is a multi-language, open-source optical character recognitionsystem originally developed by Cognitive Technologies of Moscow, Russia.Tesseract is free OCR software originally developed by Hewlett-Packardof Palo Alto, California, United States.

Still referring to FIG. 1 , in some cases, OCR may employ a two-passapproach to character recognition. A first pass may try to recognize acharacter. Each character that is satisfactory is passed to an adaptiveclassifier as training data. The adaptive classifier then gets a chanceto recognize characters more accurately as it further analyzes imagecomponents 124. Since the adaptive classifier may have learned somethinguseful a little too late to recognize characters on the first pass, asecond pass is run over the image components 124. Second pass mayinclude adaptive recognition and use characters recognized with highconfidence on the first pass to recognize better remaining characters onthe second pass. In some cases, two-pass approach may be advantageousfor unusual fonts or low-quality image components 124 where visualverbal content 140 may be distorted. Another exemplary OCR software toolinclude OCRopus. OCRopus development is led by German Research Centrefor Artificial Intelligence in Kaiserslautern, Germany. In some cases,OCR software may employ neural networks.

Still referring to FIG. 1 , in some cases, OCR may includepost-processing. For example, OCR accuracy may be increased, in somecases, if output is constrained by a lexicon. A lexicon may include alist or set of words that are allowed to occur in a document. In somecases, a lexicon may include, for instance, all the words in the Englishlanguage, or a more technical lexicon for a specific field. In somecases, an output stream may be a plain text stream or file ofcharacters. In some cases, an OCR process may preserve an originallayout of visual verbal content 140. In some cases, near-neighboranalysis can make use of co-occurrence frequencies to correct errors, bynoting that certain words are often seen together. For example,“Washington, D.C.” is generally far more common in English than“Washington DOC.” In some cases, an OCR process may make us of a prioriknowledge of grammar for a language being recognized. For example,grammar rules may be used to help determine if a word is likely to be averb or a noun. Distance conceptualization may be employed forrecognition and classification. For example, a Levenshtein distancealgorithm may be used in OCR post-processing to further optimizeresults.

With continued reference to FIG. 1 , processor 104 may be configured toclassify user to a performance category 148 of a plurality ofperformance categories 148 based on user specifications 128 andindicators 120. A “performance category”, as used in this disclosure, isan element of data representing a likelihood that a user may be hiredfor a position of a posting and successful in the position. For example,each performance category 148 may represent distinct likelihoods ofsuccess, such as a very high likelihood of success, above-averagelikelihood of success, average likelihood of success, below-averagelikelihood of success, and/or low likelihood of success. Processor 104may identify indicators 120 in user specifications 128 and/or measurethe identified indicators 120. Performance category 148 in which user isclassified may be determined by how many indicators 120 are identifiedin credentials 132 of user specifications 128 and the weight of thepresent indicators 120. Processor 104 may measure the degree indicators120 are satisfied or surpassed in user specifications 128, such as userhaving a GPA one point higher than a minimum PGA in the indicators 120.In some embodiments, aptitude score 148 may be dynamic based on acomparison of other users and/or how long posting 112 has been unfilled.For example, user may be classified in a higher performance category 148if posting 112 has been unfulfilled for a relatively long time and/orrelatively few users have applied to the posting 112, thus reflectingthat user's likelihood of being hired may be greater than under normalconditions such as an average amount of users applying to the posting112. As another example, user may be classified in a lower performancecategory 148 than user would normally be classified in if credentials132 of other users are exceptionally competitive.

In some embodiments, processor 104 may utilize a classifier, such asaptitude classifier 152, to classify user to performance category 148. A“classifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. Aptitude classifier 152 may be configured tooutput at least a datum that labels or otherwise identifies a set ofdata that are clustered together, found to be close under a distancemetric as described below, or the like. Labels may represent therespective likelihoods of success represented by each performancecategory 148 as discussed above, such as “very high likelihood ofsuccess”, “above-average likelihood of success”, “average likelihood ofsuccess”, “below-average likelihood of success”, and/or “low likelihoodof success”. Processor 104 and/or another device may generate aptitudeclassifier 152 using a classification algorithm, defined as a processwhereby processor 104 derives aptitude classifier 152 from trainingdata. In an embodiment, training data may include data from a databaseas described in this disclosure; sample and examples of userspecifications 128, credentials 132, criteria 116, and/or indicators120; indicator module 124; and any other training data describedthroughout this disclosure. In some embodiments, classifying user'scredentials 132 to performance category 148 may be based on a determinedcompatibility score 156, as described in this disclosure, and trainingdata may include compatibility scores 156. Aptitude classifier 152 maytake user specifications 128 and/or credentials 132 and criteria 116and/or indicators 120 as algorithm inputs. Aptitude classifier 152 maythen use the training data disclosed above to output data bins of userspecifications 128 to respective performance categories 148. Each databin may be categorized to each performance category 148 and labeled withthe performance category 148. Classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 1 , processor 104 may be configured to generateaptitude classifier 152 using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Processor104 may then calculate a likelihood table by calculating probabilitiesof different data entries and classification labels. Processor 104 mayutilize a naïve Bayes equation to calculate a posterior probability foreach class. A class containing the highest posterior probability is theoutcome of prediction. Naïve Bayes classification algorithm may includea gaussian model that follows a normal distribution. Naïve Bayesclassification algorithm may include a multinomial model that is usedfor discrete counts. Naïve Bayes classification algorithm may include aBernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 1 , processor 104 may be configured togenerate aptitude classifier 152 using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample- features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the aptitude classifier 152 to select thek most similar entries training data to a given sample, determining themost common aptitude classifier 152 of the entries in a database, andclassifying the known sample; this may be performed recursively and/oriteratively to generate aptitude classifier 152 that may be used toclassify input data as further samples. For instance, an initial set ofsamples may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship, which may be seeded, withoutlimitation, using expert input received according to any process asdescribed herein. As a non-limiting example, an initial heuristic mayinclude a ranking of associations between inputs and elements oftraining data. Heuristic may include selecting some number ofhighest-ranking associations and/or training data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute 1 as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)α_(i) ²)}, where aiis attribute number i of the vector. Scaling and/or normalization mayfunction to make vector comparison independent of absolute quantities ofattributes, while preserving any dependency on similarity of attributes;this may, for instance, be advantageous where cases represented intraining data are represented by different quantities of samples, whichmay result in proportionally equivalent vectors with divergent values.

Still referring to FIG. 1 , if processor 104 classifies userspecifications 128, based on first indicators 120 of a first posting112, to performance category 148 that represents a low likelihood ofsuccess for user with the first posting 112, such as “low likelihood ofsuccess”, the processor 104 may be configured to reclassify the userspecifications 128 based on second indicators 120 of a second posting112. Processor 104 may be configured to continue to classify userspecifications 128 based on indicators 120 of various postings 112 untilthe user specifications 128 are classified to performance category 148that reflects a satisfactory likelihood of success, which may be set byuser. For example, processor 104 may receive a minimum likelihood ofsuccess from user that user would be interested in and continueclassifying user specifications 128 with indicators 120 of variouspostings 112 until the user specifications are classified to performancecategory 128 that satisfies the minimum likelihood of success. In someembodiments, processor 104 may classify user specifications based on aindicators 120 of a plurality of postings 112 and rank each of thepostings 112 based on the respective performance category 148. In someembodiments, processor 104 may utilize a fuzzy set comparison toclassify user to performance category 148, which is described in detailin reference to FIG. 7 .

With continued reference to FIG. 1 , classifier and/or classificationprocess may involve use of sub-classifiers. For instance, and withoutlimitation, classifiers may classify user and/or user specifications 128to one or more types or bins of users according to one or morecategories of behavior and/or performance, such as without limitationtimeliness, punctuality, willingness to take on responsibility orinitiative, learning ability, experience, education, one or morecategories and/or types of past accomplishments and/or behaviors, or thelike, which categorizations may in turn be linked to a performancecategory 148 using a second or cascaded classifier which classifiescategorizations to performance categories, and/or one or more functionsand/or models, which may include without limitation any machine-learningmodels as described in this disclosure, that relate such categorizationsto performance categories.

Alternatively or additionally, and further referring to FIG. 1 , aclassifier may operate by matching user specifications 128 to one ormore fuzzy sets and/or one or more values of one or more linguisticvariables as described in further detail below. Linguistic variable andvalues thereof may represent categories of individual userspecifications, which categories may be matched to user specifications128 and/or related to one another and/or output linguistic variablevalues and/or any defuzzified indication of a performance category 148using one or more fuzzy inferencing systems as described in furtherdetail below. Alternatively or additionally, linguistic variable valuesmay represent categories of user and/or user data sets to which one ormore groupings of user specifications 128 may be classified, and whichmay be related to performance category 128 using one or more fuzzyinferencing systems as described in further detail below. For instance,and without limitation, classifiers may classify user and/or userspecifications 128 to one or more types or bins of users according toone or more categories of behavior and/or performance, such as withoutlimitation timeliness, punctuality, willingness to take onresponsibility or initiative, learning ability, experience, education,one or more categories and/or types of past accomplishments and/orbehaviors, or the like, which categorizations may in turn be linked to aperformance category 148 of one or more fuzzy inferencing systems asdescribed in further detail below.

With continued reference to FIG. 1 , processor 104 may be configured togenerate compatibility score 156 for user specifications 128, such asvideo record 136, based on criteria 116 and credentials 132. Processor104 may compare transcript of users specifications 128 to posting 112 togenerate compatibility score 156. In one or more embodiments, processor104 may implement a compatibility algorithm or generate a compatibilitymachine-learning module, such as compatibility module 160, to determinea compatibility score 156 as a measurement of compatibility betweencredentials 132 in video record 136 and criteria 116 in posting 112. Forthe purposes of this disclosure, a “compatibility score” is a measurablevalue representing a relevancy of credentials 132 to criteria 116. Inone or more non-limiting embodiments, compatibility score 156 may be aquantitative characteristic, such as a numerical value within a setrange. For example, a compatibility score may be a “2” for a set rangeof 1-10, where “1” represents the compatibility between credentials 132and criteria 116, and thus the compatibility between a prospectiveemployee and a posting 112, having a minimum compatibility and “10”represents credentials 132 and criteria 116 having a maximumcompatibility. In other non-limiting embodiments, compatibility score156 may be a quality characteristic, such as a color coding, where eachcolor is associated with a level of compatibility. In one or moreembodiments, if a compatibility score 156 is “low”, then a prospectiveemployee and a job position are considered to have a minimumcompatibility; if a compatibility score 156 is “high”, then prospectiveemployee and posting 112 are considered to have a maximum compatibility.Credentials 132 may be validated as described in U.S. patent applicationSer. No. 17/486,461 filed on Sep. 27, 2021, and entitled “SYSTEMS ANDMETHODS FOR SCORE GENRATION FOR APPLICANT TRACKING”, the entirety ofwhich in incorporated herein by reference. Compatibility score 156 maybe combined with and/or aggregated with other scores as described, forinstance, in U.S. patent application Ser. No. 17/486,461. Compatibilityscore 156 may be consistent with disclosure of compatibility score inU.S. patent application Ser. No. 17/582,087 filed on Jan. 24, 2022 andentitled “DIGITAL POSTING MATCH RECOMMENDATION APPARATUS AND METHODS”,which is incorporated by reference herein in its entirety.

Still referring to FIG. 1 , compatibility module 160 may use aclassifier. A “classifier,” as used in this disclosure is amachine-learning model, such as a mathematical model, neural net, orprogram generated by a machine learning algorithm known as a“classification algorithm,” as described in further detail below, thatsorts inputs into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. A classifier may beconfigured to output at least a datum that labels or otherwiseidentifies a set of data that are clustered together, found to be closeunder a distance metric as described below, or the like. Processor 104and/or another device may generate a classifier using a classificationalgorithm, defined as a process whereby a processor 104 derives aclassifier from training data. Classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 1 , processor 104 may be configured to generatea classifier using a Naïve Bayes classification algorithm. Naïve Bayesclassification algorithm generates classifiers by assigning class labelsto problem instances, represented as vectors of element values. Classlabels are drawn from a finite set. Naïve Bayes classification algorithmmay include generating a family of algorithms that assume that the valueof a particular element is independent of the value of any otherelement, given a class variable. Naïve Bayes classification algorithmmay be based on Bayes Theorem expressed as P(A/B)=P(B/A) P(A)÷P(B),where P(A/B) is the probability of hypothesis A given data B also knownas posterior probability; P(B/A) is the probability of data B given thatthe hypothesis A was true; P(A) is the probability of hypothesis A beingtrue regardless of data also known as prior probability of A; and P(B)is the probability of the data regardless of the hypothesis. A naïveBayes algorithm may be generated by first transforming training datainto a frequency table. Processor 104 may then calculate a likelihoodtable by calculating probabilities of different data entries andclassification labels. Processor 104 may utilize a naïve Bayes equationto calculate a posterior probability for each class. A class containingthe highest posterior probability is the outcome of prediction. NaïveBayes classification algorithm may include a gaussian model that followsa normal distribution. Naïve Bayes classification algorithm may includea multinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , processor 104 may be configured togenerate classifier using a K-nearest neighbors (KNN) algorithm. A“K-nearest neighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample- features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)α_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

Processor 104 may use a supervised machine-learning model to generate acompatibility score 156 given an input of a plurality of criteria 116and an input of corresponding a plurality of credentials 132. Inputs maybe manually inputted and/or labeled to identify which of the criteria116 correspond to which of the credentials 132, causing the machine tolearn correlations between the criteria 116 and credentials 132 thatmatch for a high compatibility score 156. Processor 104 may computecompatibility score 156 associated with each pairing of criteria 116 andcredentials 132 and select pairings to minimize and/or maximize thescore, depending on whether an optimal result is represented,respectively, by a minimal and/or maximal score; a mathematicalfunction, described herein as an “objective function,” may be used byprocessor 104 to score each possible pairing. Processor 104 may paircredentials 132 of video records 136 with criteria 116 of postings 112that optimizes compatibility score 156. Generation of objective functionmay include generation of a function to score and weight factors toachieve compatibility score 156 for each feasible pairing. In someembodiments, pairings may be scored in a matrix for optimization, wherecolumns represent criteria 116 of various postings 112 and rowsrepresent credentials 132 of various video records 136 potentiallypaired therewith; each cell of such a matrix may represent a score of apairing of the corresponding criteria 116 to the correspondingcredentials 132.

With continued reference to FIG. 1 , matching a video to a posting tooptimize an objective function may include performing a greedy algorithmprocess. A “greedy algorithm” is defined as an algorithm that selectslocally optimal choices, which may or may not generate a globallyoptimal solution. For instance, computing device 104 may select pairingsso that compatibility scores 132 associated therewith are the best scorefor each video record 136 and/or for each posting 112. In such anexample, optimization may determine the combination of postings 112 suchthat each pairing includes the highest score possible.

Still referring to FIG. 1 , objective function may be formulated as alinear objective function. Which processor 104 may solve using a linearprogram such as without limitation a mixed-integer program. A “linearprogram,” as used in this disclosure, is a program that optimizes alinear objective function, given at least a constraint. For instance,and without limitation, objective function may seek to maximize a totalscore Σ_(V∈R)Σ_(s∈S)c_(vp)x_(vp), where V is the set of all videorecords 136 v, S is a set of all postings p, c_(vp) is a score of apairing of a given posting with a given video, and x_(vp) is 1 if avideo v is paired with posting p, and 0 otherwise. Continuing theexample, constraints may specify that each posting 112 is assigned toonly one user, and user is assigned only one posting 112. A mathematicalsolver may be implemented to solve for the set of feasible pairings thatmaximizes the sum of scores across all pairings; mathematical solver mayimplemented on processor 104 and/or another device in apparatus 100,and/or may be implemented on third-party solver.

With continued reference to FIG. 1 , optimizing objective function mayinclude minimizing a loss function, where a “loss function” is anexpression an output of which an optimization algorithm minimizes togenerate an optimal result. As a non-limiting example, processor 104 mayassign variables relating to a set of parameters, which may correspondto score components as described above, calculate an output ofmathematical expression using the variables, and select a pairing thatproduces an output having the lowest size, according to a givendefinition of “size,” of the set of outputs representing each ofplurality of candidate ingredient combinations; size may, for instance,included absolute value, numerical size, or the like. Selection ofdifferent loss functions may result in identification of differentpotential pairings as generating minimal outputs. Objectives representedin an objective function and/or loss function may include minimizationof delivery times. Objectives may include the highest possiblecompatibility score 156 for each posting 112 on an individual basis, thehighest possible compatibility score 156 for each video record 136 on anindividual basis, and/or the highest average compatibility scores 132across all postings 112.

Still referring to FIG. 1 , in some embodiments, processor 104 may querya keyword with a text search. Keyword may include words relating toskills such as C++, Java, Computer Aided Design (CAD), welding, Excel,etc. Keyword may include education background such as Master of Science(MS), Bachelor of Science (BS), Juris Doctor (JD), and the like. Textsearch may include techniques for searching a single computer-storeddocument or a collection of documents, for example in a database. Textsearch may include full-text search. Full-text search may bedistinguished from searches based on metadata or on field-basedsearching (e.g., fields such as titles, abstracts, selected sections, orbibliographical references). In an exemplary full-text search, processor104 may examine all words in every stored document as it tries to matchsearch criteria (for example, keywords). Alternatively, a text searchmay be limited to fields, such as with field-based searching.

With continued reference to FIG. 1 , in some embodiments, text searchingmay include querying. Database may be implemented, without limitation,as a relational database, a key-value retrieval database such as a NOSQLdatabase, or any other format or structure for use as a database that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. Database may alternatively or additionallybe implemented using a distributed data storage protocol and/or datastructure, such as a distributed hash table or the like. Database mayinclude a plurality of data entries and/or records as described above.Data entries in a database may be flagged with or linked to one or moreadditional elements of information, which may be reflected in data entrycells and/or in linked tables such as tables related by one or moreindices in a relational database. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which data entries in a database may store, retrieve, organize,and/or reflect data and/or records as used herein, as well as categoriesand/or populations of data consistently with this disclosure. In somecases, querying of at least a video element may include any number ofquerying tools, including without limitation keywords (as describedabove), field-restricted search, Boolean queries, phrase search, conceptsearch, concordance search, proximity search, regular expression, fuzzysearch, wildcard search, and the like. In some cases, keywords may beused to perform a query. In some cases, a document (or trained indexers)may supply a list of words that describe subject of the document,including without limitation synonyms of words that describe thesubject. In some cases, keywords may improve recall, for instance if thekeyword list includes a keyword that is not in text of a document. Insome cases, querying tools may include field-restricted search. Afield-restricted search may allow a queries scope to be limited towithin a particular field within a stored data record, such as “Title”or “Author.” In some cases, a query tool may include Boolean queries.Searches that use Boolean operators (for example, “encyclopedia” AND“online” NOT “Encarta”) can dramatically increase precision of a search.In some cases, an AND operator may say, in effect, “Do not retrieve anydocument unless it contains both of these terms.” In some cases, a NOToperator may say, in effect, “Do not retrieve any document that containsthis word.” In some cases, a retrieval list retrieving too fewdocuments, may prompt and OR operator to be used in place of an ANDoperator to increase recall; consider, for example, “encyclopedia” AND“online” OR “Internet” NOT “Encarta.” This search will retrievedocuments about online encyclopedias that use the term “Internet”instead of “online.” In some cases, search precision and recall areinterdependent and negatively correlated in text searching. In somecases, a query tool may include phrase search. In some cases, a phrasesearch may match only those documents that contain a specified phrase.In some cases, a query tool may include a concept search. In some cases,a concept search may be based on multi-word concepts, for examplecompound term processing. In some cases, a query tool may include aconcordance search. In some cases, a concordance search may produce analphabetical list of all principal words that occur in a text and mayinclude their immediate context. In some cases, a query tool may includea proximity search. In some cases, a proximity search matches only thosedocuments that contain two or more words that are separated by aspecified number of words, are in the same sentence, or an in the sameparagraph. A query tool may include a regular expression. In some cases,a regular expression may employ a complex but powerful querying syntaxthat can be used to specify retrieval conditions with precision, forinstance database syntax. A query tool may include a fuzzy search. Insome cases, a fuzzy search may search for a document that matches giventerms while allowing for some variation around them. In some cases, aquery tool may include a wildcard search. In some cases, a wildcardsearch may substitute one or more characters in a search query for awildcard character such as an asterisk. For example, using a wildcard,such as an asterisk, in a search query “s*n” will search for termsinclusive of “sin,” “son,” “sun,” and the like.

With continued reference to FIG. 1 , for the purposes of thisdisclosure, a “compatibility algorithm” is an algorithm that determinesthe relevancy of a prospective employee's characteristics, according tocredentials 132 in video record 136, with qualifications of a jobposition, according to criteria 116 in posting 112. Compatibilityalgorithm may include machine-learning processes that are used tocalculate one or more compatibility scores 132. Machine-learning processmay be trained by using training data associated with past calculationsand corresponding credentials 132 and criteria 116. Compatibility score156 may be determined by, for example, if a certain numerical valueand/or percentage of criteria 116 are satisfied by credentials 132,where the more employment position data that matches user data, thehigher the score and the greater the compatibility between prospectiveemployee and posting 112. For example, and without limitation, criteria116 of posting 112 may include a qualification of requiring a teacherwith at least five years of work experience, and credentials 132 invideo record 136 of the teacher may include seven years of workexperience in teaching, then a numerical value representingcompatibility score 156 may be increased due to the data correlating,thus indicating that prospective employee has a high compatibility forthe job position. In some embodiments, processor 104 may distinguishbetween criteria 116 that are requirements and criteria 116 that arepreferences. For example, a greater weight, and therefore a greaterimpact on compatibility score 156, may be given to credentials 132 thatmatch criteria 116 that are requirements than credentials 132 that matchcriteria 116 that are preferences. In some embodiments, compatibilityscore 156 may include a compatibility score for requirements, acompatibility score for preferences, and/or a comprehensivecompatibility score for all criteria 116. In some embodiments,compatibility score 156 for video record 136 with credentials 132 thatdo not satisfy all criteria 116 of posting 112 that are requirements maybe zero, a non-score, and/or otherwise distinguished from video records136 with credentials 132 that do satisfy all criteria 116 that arerequirements. As used in this disclosure, a criterion being “satisfied”means that one or more credentials meets or exceeds the criterion. Forexample, a criterion requiring five years' experience in a given fieldis satisfied by a credential of having worked six years in the givenfield. Whether credentials 132 satisfy criteria 116 may be determined byan algorithm discussed in this disclosure such as compatibilityalgorithm, a machine-learning process discussed in this disclosure,and/or the like applied to posting 112. For example, dates, numbers,and/or words describing lengths of time may be Keywords that areidentified, processor 104 may calculate the length of time described ifnot facially apparent, processor 104 may determine from neighboring textthe significance of the period of time, which may include identifyingneighboring Keywords. If processor 104 determines that criteria 116includes an amount of time of experience in a field, then processor 104may use the same algorithm and/or machine-learning process to identifyinformation in video record 136 addressing the criteria 116 and analyzewhether credentials 132 satisfy the criteria 116. In an embodiment,compatibility algorithm may be received from a remote device. In someembodiments, compatibility algorithm is generated by processor 104. Inone or more embodiments, compatibility algorithm may be generated as afunction of credentials 132 and/or criteria 116.

In one or more embodiments, a machine-learning process may be used todetermine compatibility algorithm or to generate a machine-learningmodel that may directly calculate compatibility score 156. In one ormore embodiments, a machine-learning model may be generated usingtraining data. Training data may include inputs and correspondingpredetermined outputs so that a machine-learning module may use thecorrelations between the provided exemplary inputs and outputs todevelop an algorithm and/or relationship that then allows themachine-learning module to determine its own outputs for inputs.Training data may contain correlations that a machine-learning processmay use to model relationships between two or more categories of dataelements. The exemplary inputs and outputs may come from a database,such as any database described in this disclosure, or be provided by auser, such as a prospective employee and/or an employer. In otherembodiments, a machine-learning module may obtain a training set byquerying a communicatively connected database that includes past inputsand outputs. Training data may include inputs from various types ofdatabases, resources, and/or user inputs and outputs correlated to eachof those inputs so that a machine-learning module may determine anoutput, such as compatibility score 156, for an input, such as criteria116 and credentials 132. Training data may be obtained from and/or inthe form of previous posting-video record matches. Previous video recordmatches may include resumes such as video resumes and written resumes.Correlations may indicate causative and/or predictive links betweendata, which may be modeled as relationships, such as mathematicalrelationships, by machine-learning processes, as described in furtherdetail below. In one or more embodiments, training data may be formattedand/or organized by categories of data elements by, for example,associating data elements with one or more descriptors corresponding tocategories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements.

Referring now to FIG. 2 , an exemplary embodiment of neural network 200is illustrated. A neural network 200 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.”

Referring now to FIG. 3 , an exemplary embodiment of a node 300 of aneural network is illustrated. Node 300 may include, without limitationa plurality of inputs x_(i) that may receive numerical values frominputs to a neural network containing the node and/or from other nodes.Node may perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Referring now to FIG. 4 , an exemplary embodiment of an immutablesequential listing 400 is illustrated. Data elements are listing inimmutable sequential listing 400; data elements may include any form ofdata, including textual data, image data, encrypted data,cryptographically hashed data, and the like. Data elements may include,without limitation, one or more at least a digitally signed assertion.In one embodiment, a digitally signed assertion 404 is a collection oftextual data signed using a secure proof as described in further detailbelow; secure proof may include, without limitation, a digitalsignature. Collection of textual data may contain any textual data,including without limitation American Standard Code for InformationInterchange (ASCII), Unicode, or similar computer-encoded textual data,any alphanumeric data, punctuation, diacritical mark, or any characteror other marking used in any writing system to convey information, inany form, including any plaintext or cyphertext data; in an embodiment,collection of textual data may be encrypted, or may be a hash of otherdata, such as a root or node of a Merkle tree or hash tree, or a hash ofany other information desired to be recorded in some fashion using adigitally signed assertion 404. In an embodiment, collection of textualdata states that the owner of a certain transferable item represented ina digitally signed assertion 404 register is transferring that item tothe owner of an address. A digitally signed assertion 404 may be signedby a digital signature created using the private key associated with theowner's public key, as described above.

Still referring to FIG. 4 , in some embodiments, an address is a textualdatum identifying the recipient of virtual currency or another item ofvalue, such as user specifications 128, in a digitally signed assertion404. In some embodiments, address may be linked to a public key, thecorresponding private key of which is owned by the recipient of adigitally signed assertion 404. For instance, address may be the publickey. Address may be a representation, such as a hash, of the public key.Address may be linked to the public key in memory of a computing device,for instance via a “wallet shortener” protocol. Where address is linkedto a public key, a transferee in a digitally signed assertion 404 mayrecord a subsequent a digitally signed assertion 404 transferring someor all of the value transferred in the first a digitally signedassertion 404 to a new address in the same manner. A digitally signedassertion 404 may contain textual information that is not a transfer ofsome item of value in addition to, or as an alternative to, such atransfer. For instance, as described in further detail below, adigitally signed assertion 404 may indicate a confidence levelassociated with a distributed storage node as described in furtherdetail below.

In an embodiment, and still referring to FIG. 4 immutable sequentiallisting 400 records a series of at least a posted content in a way thatpreserves the order in which the at least a posted content took place.Temporally sequential listing may be accessible at any of varioussecurity settings; for instance, and without limitation, temporallysequential listing may be readable and modifiable publicly, may bepublicly readable but writable only by entities and/or devices havingaccess privileges established by password protection, confidence level,or any device authentication procedure or facilities described herein,or may be readable and/or writable only by entities and/or deviceshaving such access privileges. Access privileges may exist in more thanone level, including, without limitation, a first access level orcommunity of permitted entities and/or devices having ability to read,and a second access level or community of permitted entities and/ordevices having ability to write; first and second community may beoverlapping or non-overlapping. In an embodiment, posted content and/orimmutable sequential listing 400 may be stored as one or more zeroknowledge sets (ZKS), Private Information Retrieval (PIR) structure, orany other structure that allows checking of membership in a set byquerying with specific properties. Such database may incorporateprotective measures to ensure that malicious actors may not query thedatabase repeatedly in an effort to narrow the members of a set toreveal uniquely identifying information of a given posted content.

Still referring to FIG. 4 , immutable sequential listing 400 maypreserve the order in which the at least a posted content took place bylisting them in chronological order; alternatively or additionally,immutable sequential listing 400 may organize digitally signedassertions 404 into sub-listings 408 such as “blocks” in a blockchain,which may be themselves collected in a temporally sequential order;digitally signed assertions 404 within a sub-listing 408 may or may notbe temporally sequential. Posting 112 with criteria 116, userspecifications 128 with credentials 132, and/or aptitude score 148 maybe posted on immutable sequential listing 400, such as blockchain.Training data for any machine-learning module discussed in thisdisclosure may be posted on immutable sequential listing 400, such asblockchain. A master list may be included. Master list may include ahash-table and/or distributed hash table which may be used to locate arequestor-linked data store. For example, a public key associated with arequestor containing location information pertaining to requestor-linkeddata store may be converted into a series of hash functions. This mayoccur by converting an entry into a series of integers by using a hashfunction. A hash function may include any function that may be used tomap a set of data which falls into the hash table. Hash functions may bestored in a hash table, where it can be quickly retrieved using a hashedkey. The hashed key may then be used to access requestor-linked datastore when prompted. Using the hashed key, a hash function may computean index that may suggest where requestor-linked data store may befound. Locating may also be performed by linking the at least anencrypted data record to a digital signature associated with therequestor. Requestor may produce a digital signature, which may then belinked to the at least an encrypted data record and locate to thelocation of the at least an encrypted data record. When the digitalsignature is presented, it may contain location information of the atleast an encrypted data record and allow access control regulator tolocate the precise location of encrypted data record. For example,digital signature may be generated using a public and/or private keylinked to requestor which may contain location information of encrypteddata record. In an embodiment, encrypted data record may be linked to arequestor key, so that when a requestor key is presented, location ofencrypted data record becomes apparent. Locating may also be performedby information that may be contained in data access request. Forexample, a data access request associated with a user may containlocation information of encrypted data record that requestor isattempting to access. When generating a data access request, requestormay specify the location of encrypted data record that may then betransmitted to access control regulator. Additional disclosurepertaining to immutable sequential listing can be found in U.S. patentapplication Ser. No. 17/486,461 filed on Sep. 27, 2021, and entitled“SYSTEMS AND METHODS FOR SCORE GENRATION FOR APPLICANT TRACKING”, theentirety of which in incorporated herein by reference.

With continued reference to FIG. 4 , the ledger may preserve the orderin which at least a posted content took place by listing them insub-listings 408 and placing the sub-listings 408 in chronologicalorder. The immutable sequential listing 400 may be a distributed,consensus-based ledger, such as those operated according to theprotocols promulgated by Ripple Labs, Inc., of San Francisco, Calif., orthe Stellar Development Foundation, of San Francisco, Calif, or ofThunder Consensus. In some embodiments, the ledger is a secured ledger;in one embodiment, a secured ledger is a ledger having safeguardsagainst alteration by unauthorized parties. The ledger may be maintainedby a proprietor, such as a system administrator on a server, thatcontrols access to the ledger; for instance, the user account controlsmay allow contributors to the ledger to add at least a posted content tothe ledger, but may not allow any users to alter at least a postedcontent that have been added to the ledger. In some embodiments, ledgeris cryptographically secured; in one embodiment, a ledger iscryptographically secured where each link in the chain containsencrypted or hashed information that makes it practically infeasible toalter the ledger without betraying that alteration has taken place, forinstance by requiring that an administrator or other party sign newadditions to the chain with a digital signature. Immutable sequentiallisting 400 may be incorporated in, stored in, or incorporate, anysuitable data structure, including without limitation any database,datastore, file structure, distributed hash table, directed acyclicgraph or the like. In some embodiments, the timestamp of an entry iscryptographically secured and validated via trusted time, eitherdirectly on the chain or indirectly by utilizing a separate chain. Inone embodiment the validity of timestamp is provided using a timestamping authority as described in the RFC 3161 standard for trustedtimestamps, or in the ANSI ASC x9.95 standard. In another embodiment,the trusted time ordering is provided by a group of entitiescollectively acting as the time stamping authority with a requirementthat a threshold number of the group of authorities sign the timestamp.Immutable sequential listing 400 and/or any component of the immutablesequential listing 400, such as sub-listing 408 and digitally signedassertions 404, may be validated by processor 104 consistent withdisclosure of validation in U.S. patent application Ser. No. 16/698,182filed on Nov. 27, 2019 and titled “SYSTEMS AND METHODS FOR BIOMETRIC KEYGENERATION IN DATA ACCESS CONTROL, DATA VERIFICATION, AND PATH SELECTIONIN BLOCK CHAIN-LINKED WORKFORCE DATA MANAGEMENT”, which is incorporatedby reference herein in its entirety.

In some embodiments, and with continued reference to FIG. 4 , immutablesequential listing 400, once formed, may be inalterable by any party, nomatter what access rights that party possesses. For instance, immutablesequential listing 400 may include a hash chain, in which data is addedduring a successive hashing process to ensure non-repudiation. Immutablesequential listing 400 may include a block chain. In one embodiment, ablock chain is immutable sequential listing 400 that records one or morenew at least a posted content in a data item known as a sub-listing 408or “block.” An example of a block chain is the BITCOIN block chain usedto record BITCOIN transactions and values. Sub-listings 408 may becreated in a way that places the sub-listings 408 in chronological orderand link each sub-listing 408 to a previous sub-listing 408 in thechronological order so that any computing device may traverse thesub-listings 408 in reverse chronological order to verify any at least aposted content listed in the block chain. Each new sub-listing 408 maybe required to contain a cryptographic hash describing the previoussub-listing 408. In some embodiments, the block chain may contain asingle first sub-listing 408 sometimes known as a “genesis block.”

Still referring to FIG. 4 , the creation of a new sub-listing 408 may becomputationally expensive; for instance, the creation of a newsub-listing 408 may be designed by a “proof of work” protocol acceptedby all participants in forming the immutable sequential listing 400 totake a powerful set of computing devices a certain period of time toproduce. Where one sub-listing 408 takes less time for a given set ofcomputing devices to produce the sub-listing 408, protocol may adjustthe algorithm to produce the next sub-listing 408 so that it willrequire more steps; where one sub-listing 408 takes more time for agiven set of computing devices to produce the sub-listing 408, protocolmay adjust the algorithm to produce the next sub-listing 408 so that itwill require fewer steps. As an example, protocol may require a newsub-listing 408 to contain a cryptographic hash describing its contents;the cryptographic hash may be required to satisfy a mathematicalcondition, achieved by having the sub-listing 408 contain a number,called a nonce, whose value is determined after the fact by thediscovery of the hash that satisfies the mathematical condition.Continuing the example, the protocol may be able to adjust themathematical condition so that the discovery of the hash describing asub-listing 408 and satisfying the mathematical condition requires moreor less steps, depending on the outcome of the previous hashing attempt.Mathematical condition, as an example, might be that the hash contains acertain number of leading zeros and a hashing algorithm that requiresmore steps to find a hash containing a greater number of leading zeros,and fewer steps to find a hash containing a lesser number of leadingzeros. In some embodiments, production of a new sub-listing 408according to the protocol is known as “mining.” The creation of a newsub-listing 408 may be designed by a “proof of stake” protocol as willbe apparent to those skilled in the art upon reviewing the entirety ofthis disclosure.

Continuing to refer to FIG. 4 , in some embodiments, protocol alsocreates an incentive to mine new sub-listings 408. The incentive may befinancial; for instance, successfully mining a new sub-listing 408 mayresult in the person or entity that mines the sub-listing 408 receivinga predetermined amount of currency. The currency may be fiat currency.Currency may be cryptocurrency as defined below. In other embodiments,incentive may be redeemed for particular products or services; theincentive may be a gift certificate with a particular business, forinstance. In some embodiments, incentive is sufficiently attractive tocause participants to compete for the incentive by trying to race eachother to the creation of sub-listings 408. Each sub-listing 408 createdin immutable sequential listing 400 may contain a record or at least aposted content describing one or more addresses that receive anincentive, such as virtual currency, as the result of successfullymining the sub-listing 408.

With continued reference to FIG. 4 , where two entities simultaneouslycreate new sub-listings 408, immutable sequential listing 400 maydevelop a fork; protocol may determine which of the two alternatebranches in the fork is the valid new portion of the immutablesequential listing 400 by evaluating, after a certain amount of time haspassed, which branch is longer. “Length” may be measured according tothe number of sub-listings 408 in the branch. Length may be measuredaccording to the total computational cost of producing the branch.Protocol may treat only at least a posted content contained in the validbranch as valid at least a posted content. When a branch is foundinvalid according to this protocol, at least a posted content registeredin that branch may be recreated in a new sub-listing 408 in the validbranch; the protocol may reject “double spending” at least a postedcontent that transfer the same virtual currency that another at least aposted content in the valid branch has already transferred. As a result,in some embodiments the creation of fraudulent at least a posted contentrequires the creation of a longer immutable sequential listing 400branch by the entity attempting the fraudulent at least a posted contentthan the branch being produced by the rest of the participants; as longas the entity creating the fraudulent at least a posted content islikely the only one with the incentive to create the branch containingthe fraudulent at least a posted content, the computational cost of thecreation of that branch may be practically infeasible, guaranteeing thevalidity of all at least a posted content in the immutable sequentiallisting 400.

Still referring to FIG. 4 , additional data linked to at least a postedcontent may be incorporated in sub-listings 408 in the immutablesequential listing 400; for instance, data may be incorporated in one ormore fields recognized by block chain protocols that permit a person orcomputer forming a at least a posted content to insert additional datain the immutable sequential listing 400. In some embodiments, additionaldata is incorporated in an unspendable at least a posted content field.For instance, the data may be incorporated in an OP_RETURN within theBITCOIN block chain. In other embodiments, additional data isincorporated in one signature of a multi-signature at least a postedcontent. In an embodiment, a multi-signature at least a posted contentis at least a posted content to two or more addresses. In someembodiments, the two or more addresses are hashed together to form asingle address, which is signed in the digital signature of the at leasta posted content. In other embodiments, the two or more addresses areconcatenated. In some embodiments, two or more addresses may be combinedby a more complicated process, such as the creation of a Merkle tree orthe like. In some embodiments, one or more addresses incorporated in themulti-signature at least a posted content are typical crypto-currencyaddresses, such as addresses linked to public keys as described above,while one or more additional addresses in the multi-signature at least aposted content contain additional data related to the at least a postedcontent; for instance, the additional data may indicate the purpose ofthe at least a posted content, aside from an exchange of virtualcurrency, such as the item for which the virtual currency was exchanged.In some embodiments, additional information may include networkstatistics for a given node of network, such as a distributed storagenode, e.g. the latencies to nearest neighbors in a network graph, theidentities or identifying information of neighboring nodes in thenetwork graph, the trust level and/or mechanisms of trust (e.g.certificates of physical encryption keys, certificates of softwareencryption keys, (in non-limiting example certificates of softwareencryption may indicate the firmware version, manufacturer, hardwareversion and the like), certificates from a trusted third party,certificates from a decentralized anonymous authentication procedure,and other information quantifying the trusted status of the distributedstorage node) of neighboring nodes in the network graph, IP addresses,GPS coordinates, and other information informing location of the nodeand/or neighboring nodes, geographically and/or within the networkgraph. In some embodiments, additional information may include historyand/or statistics of neighboring nodes with which the node hasinteracted. In some embodiments, this additional information may beencoded directly, via a hash, hash tree or other encoding.

Now referring to FIG. 5 , an exemplary embodiment of a method 500 forsuccess probability determination for a user is illustrated. At step505, processor receives a plurality of criteria; this may beimplemented, without limitation, as described above in reference toFIGS. 1-5 .

At step 510, processor generates indicators as a function of criteria;this may be implemented, without limitation, as described above inreference to FIGS. 1-5 . Generating indicators may include utilizingmachine-learning module to generate the indicators. Indicators may beweighted based on posting and/or requirements of criteria. Weight ofindicators may be determined by machine-learning module. Indicators maybe based on a level of posting.

At step 515, processor receives user specifications includingcredentials of user; this may be implemented, without limitation, asdescribed above in reference to FIGS. 1-5 . User specifications mayinclude video record.

At step 520, processor classifies user specifications, using classifier,to performance category of plurality of performance categories based onuser specifications and indicators; this may be implemented, withoutlimitation, as described above in reference to FIGS. 1-5 . Processor maybe configured to generate compatibility score, wherein classifying theuser specifications is based on the compatibility score. Processor maybe configured to identify indicators in user specification. Processormay be configured to measure the degree indicators are satisfied in userspecifications.

Referring now to FIG. 6 , an exemplary embodiment of a machine-learningmodule 600 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 604 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 608 given data provided as inputs 612;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 6 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 604 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 604 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 604 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 604 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 604 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 604 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data604 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 6 ,training data 604 may include one or more elements that are notcategorized; that is, training data 604 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 604 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 604 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 604 used by machine-learning module 600 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, input data may include user information such as useridentification, and output data may include one or more sets of useractivity data.

Further referring to FIG. 6 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 616. Training data classifier 616 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 600 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 604. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 616 may classify elements of training data to userand/or user activity data.

Still referring to FIG. 6 , machine-learning module 600 may beconfigured to perform a lazy-learning process 620 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 604. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 604 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 6 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 624. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 624 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 624 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 604set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 6 , machine-learning algorithms may include atleast a supervised machine-learning process 628. At least a supervisedmachine-learning process 628, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayuser information such as user identification as described above asinputs, one or more sets of user activity data as outputs, and a scoringfunction representing a desired form of relationship to be detectedbetween inputs and outputs; scoring function may, for instance, seek tomaximize the probability that a given input and/or combination ofelements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 604. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 628 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 6 , machine learning processes may include atleast an unsupervised machine-learning processes 632. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 6 , machine-learning module 600 may be designedand configured to create a machine-learning model 624 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 6 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring to FIG. 7 , an exemplary embodiment of fuzzy set comparison700 is illustrated. A first fuzzy set 704 may be represented, withoutlimitation, according to a first membership function 708 representing aprobability that an input falling on a first range of values 712 is amember of the first fuzzy set 704, where the first membership function708 has values on a range of probabilities such as without limitationthe interval [0,1], and an area beneath the first membership function708 may represent a set of values within first fuzzy set 704. Althoughfirst range of values 712 is illustrated for clarity in this exemplarydepiction as a range on a single number line or axis, first range ofvalues 712 may be defined on two or more dimensions, representing, forinstance, a Cartesian product between a plurality of ranges, curves,axes, spaces, dimensions, or the like. First membership function 708 mayinclude any suitable function mapping first range 712 to a probabilityinterval, including without limitation a triangular function defined bytwo linear elements such as line segments or planes that intersect at orbelow the top of the probability interval. As a non-limiting example,triangular membership function may be defined as:

${y\left( {x,a,b,c} \right)} = \left\{ \begin{matrix}{0,{{{for}x} > {c{and}x} < a}} \\{\frac{x - a}{b - a},{{{for}a} \leq x < b}} \\{\frac{c - x}{c - b},{{{if}b} < x \leq c}}\end{matrix} \right.$

a trapezoidal membership function may be defined as:

${y\left( {x,a,b,c,d} \right)} = {\max\left( {{\min\left( {\frac{x - a}{b - a},1,\frac{d - x}{d - c}} \right)},0} \right)}$

a sigmoidal function may be defined as:

${y\left( {x,a,c} \right)} = \frac{1}{1 - e^{- {a({x - c})}}}$

a Gaussian membership function may be defined as:

${y\left( {x,c,\sigma} \right)} = e^{{- \frac{1}{2}}{(\frac{x - c}{\sigma})}^{2}}$

and a bell membership function may be defined as:

${y\left( {x,a,b,c,} \right)} = \left\lbrack {1 + {❘\frac{x - c}{a}❘}^{2b}} \right\rbrack^{- 1}$

Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various alternative or additionalmembership functions that may be used consistently with this disclosure.

Still referring to FIG. 7 , first fuzzy set 704 may represent any valueor combination of values as described above, including output from oneor more machine-learning models and user specifications 128 andindicators 120, a predetermined class, such as without limitationperformance category 148. A second fuzzy set 716, which may representany value which may be represented by first fuzzy set 704, may bedefined by a second membership function 720 on a second range 724;second range 724 may be identical and/or overlap with first range 712and/or may be combined with first range via Cartesian product or thelike to generate a mapping permitting evaluation overlap of first fuzzyset 704 and second fuzzy set 716. Where first fuzzy set 704 and secondfuzzy set 716 have a region 728 that overlaps, first membership function708 and second membership function 720 may intersect at a point 732representing a probability, as defined on probability interval, of amatch between first fuzzy set 704 and second fuzzy set 716.Alternatively or additionally, a single value of first and/or secondfuzzy set may be located at a locus 736 on first range 712 and/or secondrange 724, where a probability of membership may be taken by evaluationof first membership function 708 and/or second membership function 720at that range point. A probability at 728 and/or 732 may be compared toa threshold 740 to determine whether a positive match is indicated.Threshold 740 may, in a non-limiting example, represent a degree ofmatch between first fuzzy set 704 and second fuzzy set 716, and/orsingle values therein with each other or with either set, which issufficient for purposes of the matching process; for instance, thresholdmay indicate a sufficient degree of overlap between an output from oneor more machine-learning models and/or user specifications 128 andindicators 120 and a predetermined class, such as without limitationperformance category 148, for combination to occur as described above.Alternatively or additionally, each threshold may be tuned by amachine-learning and/or statistical process, for instance and withoutlimitation as described in further detail below.

Further referring to FIG. 7 , in an embodiment, a degree of matchbetween fuzzy sets may be used to classify user specifications 128 andindicators 120 with performance category 148. For instance, if userspecifications 128 and indicators 120 has a fuzzy set matchingperformance category 148 fuzzy set by having a degree of overlapexceeding a threshold, computing device 104 may classify the userspecifications 128 and indicators 120 as belonging to the performancecategory 148. Where multiple fuzzy matches are performed, degrees ofmatch for each respective fuzzy set may be computed and aggregatedthrough, for instance, addition, averaging, or the like, to determine anoverall degree of match.

Still referring to FIG. 7 , in an embodiment, a user specifications 128and indicators 120 may be compared to multiple performance category 148fuzzy sets. For instance, user specifications 128 and indicators 120 maybe represented by a fuzzy set that is compared to each of the multipleperformance category 148 fuzzy sets; and a degree of overlap exceeding athreshold between the user specifications 128 and indicators 120 fuzzyset and any of the multiple performance category 148 fuzzy sets maycause computing device 104 to classify the user specifications 128 andindicators 120 as belonging to performance category 148. For instance,in one embodiment there may be two performance category 148 fuzzy sets,representing respectively very high likelihood of success andabove-average likelihood of success. First performance category 148 mayhave a first fuzzy set; Second performance category 148 may have asecond fuzzy set; and user specifications 128 and indicators 120 mayhave a user specifications 128 and indicators 120 fuzzy set. Computingdevice 104, for example, may compare a user specifications 128 andindicators 120 fuzzy set with each of first performance category 148fuzzy set and second performance category 148 fuzzy set, as describedabove, and classify a user specifications 128 and indicators 120 toeither, both, or neither of first performance category 148 or secondperformance category 148. Machine-learning methods as describedthroughout may, in a non-limiting example, generate coefficients used infuzzy set equations as described above, such as without limitation x, c,and σ of a Gaussian set as described above, as outputs ofmachine-learning methods. Likewise, user specifications 128 andindicators 120 may be used indirectly to determine a fuzzy set, as userspecifications 128 and indicators 120 fuzzy set may be derived fromoutputs of one or more machine-learning models that take the userspecifications 128 and indicators 120 directly or indirectly as inputs.User specifications 128 may alternatively be related to one or morelinguistic variable values as described above, which may then be relatedto performance categories using fuzzy inferencing systems,defuzzification processes, or the like.

Still referring to FIG. 7 , processor 104 may use a logic comparisonprogram, such as, but not limited to, a fuzzy logic model to determineperformance category 148. Each performance category 148 may berepresented as a value for a linguistic variable representingperformance category 148, such as without limitation, “very highlikelihood of success”, “above-average likelihood of success”, “averagelikelihood of success”, “below-average likelihood of success”, “lowlikelihood of success”, and the like; in other words a fuzzy set asdescribed above that corresponds to a likelihood of success for user toreceive position of posting 112 and/or a likelihood user will excel inthe position, as calculated using any statistical, machine-learning, orother method that may occur to a person skilled in the art uponreviewing the entirety of this disclosure. In other words, a givenelement of user specifications 128 and indicators 120 may have a firstnon-zero value for membership in a first linguistic variable value suchas “very high likelihood of success,” and a second non-zero value formembership in a second linguistic variable value such as “above-averagelikelihood of success.” In some embodiments, determining performancecategory 148 may include using a linear regression model. A linearregression model may include a machine learning model. A linearregression model may be configured to map data of user specifications128 and indicators 120, such as credentials 132 and/or criteria 116, toone or more performance categories 148. A linear regression model may betrained using user specifications 128, indicators 120, and performancecategories 148. A linear regression model may map statistics such as,but not limited to, a percentage of users who apply to postings 112within performance category 148 and are offered the position. In someembodiments, determining performance category 148 of user specifications128 and indicators 120 may include using performance category 148classification model. Performance category 148 classification model maybe configured to input collected data and cluster data to a centroidbased on, but not limited to, frequency of appearance, linguisticindicators of likelihood of success of use, and the like. Centroids mayinclude scores assigned to them such that credentials 132 and/orcriteria 112 may each be assigned a score. In some embodiments,performance category 148 classification model may include a K-meansclustering model. In some embodiments, a performance category 148classification model may include a particle swarm optimization model. Insome embodiments, determining a performance category 148 of userspecifications 128 and indicators 120 may include using a fuzzyinference engine. A fuzzy inference engine may be configured to map oneor more user specifications 128 and indicators 120 data elements usingfuzzy logic. In some embodiments, a plurality of entity assessmentdevices may be arranged by a logic comparison program into performancecategory 148 arrangements. A “performance category 148 arrangement” asused in this disclosure is any grouping of objects and/or data based onskill level and/or output score. This step may be implemented asdescribed above in FIGS. 1-6 . Membership function coefficients and/orconstants as described above may be tuned according to classificationand/or clustering algorithms. For instance, and without limitation, aclustering algorithm may determine a Gaussian or other distribution ofquestions about a centroid corresponding to a given level of likelihoodof success of user, and an iterative or other method may be used to finda membership function, for any membership function type as describedabove, that minimizes an average error from the statistically determineddistribution, such that, for instance, a triangular or Gaussianmembership function about a centroid representing a center of thedistribution that most closely matches the distribution. Error functionsto be minimized, and/or methods of minimization, may be performedwithout limitation according to any error function and/or error functionminimization process and/or method as described in this disclosure.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods andapparatus according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. An apparatus for success probability determination for a user, theapparatus comprising: at least a processor communicatively connected toa user device; and a memory communicatively connected to the processor,the memory containing instructions configuring the at least a processorto: receive a plurality of criteria; generate indicators as a functionof the criteria, wherein generating the indicators comprises: training,iteratively, a machine learning model using training data and a machinelearning algorithm, wherein the training data includes criteria datacorrelated with indicator data; updating the training data with inputand output results from the trained machine learning model andretraining the machine learning model with the updated training datausing a feedback loop; generating the indicators using the retrainedmachine learning model, wherein the plurality of criteria are providedas an input to the retrained machine learning model to output theindicators; and determining a weight of the indicators using the machinelearning model; receive user specifications, the user specificationscomprising credentials of a user; classify the user specifications,using a classifier, wherein the classifier comprises a sub-classifier,to a performance category of a plurality of performance categories basedon the user specifications and the indicators; determine a success ofplacement for each of one or more previous entities in one or moreprevious positions, wherein the success of placement includes at leastone commonality between the one or more previous entities and the userspecifications, wherein the at least one commonality is modeled as acausative link between at least one of the plurality of criteria and theclassified user specifications; determine a relevancy of the classifieduser specifications as a function of qualifications of a job positionand the success of placement for each of the one or more previousentities utilizing a second machine learning model.
 2. The apparatus ofclaim 1, wherein generating the indicators comprises utilizing amachine-learning module to generate the indicators.
 3. The apparatus ofclaim 1, wherein the indicators are weighted based on the criteria. 4.The apparatus of claim 1, wherein the criteria comprise requirements,wherein the indicators are weighted based on the requirements. 5.(canceled).
 6. The apparatus of claim 1, wherein the processor isconfigured to generate a compatibility score, wherein classifying theuser specifications is based on the compatibility score.
 7. Theapparatus of claim 1, wherein the indicators are based on a level of thecriteria.
 8. The apparatus of claim 1, wherein the processor isconfigured to identify the indicators in the user specifications.
 9. Theapparatus of claim 1, wherein the processor is configured to measure thedegree the indicators are satisfied in the user specifications.
 10. Theapparatus of claim 1, wherein the user specifications comprise a videorecord.
 11. A method for success probability determination for a user,the method comprising: receiving, at a processor, a plurality ofcriteria; generating, by the processor, indicators as a function of thecriteria, wherein generating the indicators comprises: training,iteratively a machine learning model using training data and a machinelearning algorithm, wherein the training data includes criteria datacorrelated with indicator data; updating the training data with inputand output results from the trained machine learning model andretraining the machine learning model with the updated training datausing a feedback loop; generating the indicators using the retrainedmachine learning model, wherein the plurality of criteria are providedas an input to the retrained machine learning model to output theindicators; and determining a weight of the indicators using the machinelearning model; receiving, at the processor, user specifications, theuser specifications comprising credentials of a user; classifying, bythe processor using a classifier, wherein the classifier comprises asub-classifier, the user specifications to a performance category of aplurality of performance categories based on the user specifications andthe indicators; determining, by the processor, a success of placementfor each of one or more previous entities in one or more previouspositions, wherein the success of placement includes at least onecommonality between the one or more previous entities and the userspecifications, wherein the at least one commonality is modeled as acausative link between at least one of the plurality of criteria and theclassified user specifications; and determining a relevancy of theclassified user specifications as a function of qualifications of a jobposition and the success of placement for each of the one or moreprevious entities utilizing a second machine learning model.
 12. Themethod of claim 11, wherein generating the indicators comprisesutilizing a machine-learning module to generate the indicators.
 13. Themethod of claim 11, wherein the indicators are weighted based on thecriteria.
 14. The method of claim 11, wherein the criteria compriserequirements, wherein the indicators are weighted based on therequirements.
 15. (canceled).
 16. The method of claim 11, wherein theprocessor is configured to generate a compatibility score, whereinclassifying the user specifications is based on the compatibility score.17. The method of claim 11, wherein the indicators are based on a levelof the criteria.
 18. The method of claim 11, wherein the processor isconfigured to identify the indicators in the user specifications. 19.The method of claim 11, wherein the processor is configured to measurethe degree the indicators are satisfied in the user specifications. 20.The method of claim 11, wherein the user specifications comprise a videorecord.